native-api-py-demo

This is a demo of IRIS Native API for Python, which uses Python to call the Object Script method and flow the message in production. Python obtains the message after flow and the message in global, and uses ZPM Package deployment.

Firstly, we need to install the Native API package

Enter on the command line

Introduction

In my previous article, I introduced the FHIR Data Explorer, a proof-of-concept application that connects InterSystems IRIS, Python, and Ollama to enable semantic search and visualization over healthcare data in FHIR format, a project currently participating in the InterSystems External Language Contest.

In this follow-up, we’ll see how I integrated Ollama for generating patient history summaries directly from structured FHIR data stored in IRIS, using lightweight local language models (LLMs) such as Llama 3.2:1B or Gemma 2:2B.

The goal was to build a completely local AI pipeline that can extract, format, and narrate patient histories while keeping data private and under full control.

All patient data used in this demo comes from FHIR bundles, which were parsed and loaded into IRIS via the IRIStool module. This approach makes it straightforward to query, transform, and vectorize healthcare data using familiar pandas operations in Python. If you’re curious about how I built this integration, check out my previous article Building a FHIR Vector Repository with InterSystems IRIS and Python through the IRIStool module.

Both IRIStool and FHIR Data Explorer are available on the InterSystems Open Exchange — and part of my contest submissions. If you find them useful, please consider voting for them!

• IRIStool and Data Manager
• FHIR Data Explorer

Hi all. Today we are going to use k-means algorithm on the Iris Dataset.

Note: I have done the following on Ubuntu 18.04, Apache Zeppelin 0.8.0, python 3.6.5.

Introduction

K-Means is one of the simplest unsupervised learning algorithms that solves the clustering problem. It groups all the objects in such a way that objects in the same group (group is a cluster) are more similar (in some sense) to each other than to those in other groups. For example, assume you have an image with a red ball on the green grass. K-Means will split all pixels into two clusters.

Hi Community, 
We invite you to join our next Meetup in Cambridge, MA on August 28, 5:30-7:30 pm.
>> RSVP here <<
 

How to Build Applications with LangGraph: A Step-by-Step Guide

Tags: #LangGraph #LangChain #AI #Agents #Python #LLM #StateManagement #Workflows

Hi everyone, I want to tell you a little about LangGraph, a tool that I'm studying and developing.

Basically traditional AI applications often face challenges when dealing with complex workflows and dynamic states. LangGraph offers a robust solution, enabling the creation of stateful agents that can manage complex conversations, make context-based decisions, and execute sophisticated workflows.

Hey Developers,

Please welcome the new video by @sween on InterSystems Developers YouTube:

⏯ InterSystems IRIS Native Python API in AWS Lambda

Hi Developers, 

New video is already on InterSystems Developers YouTube: 

⏯ Embedded Python for ObjectScript Developers: Working with Python and ObjectScript Side-By-Side

  Hi, Community!

Since this article is an overview of Flask Login, let's begin with Flask Introduction!

What is Flask?

In the realm of web development, Python has emerged as a formidable force, offering its versatility and robustness to create dynamic and scalable applications. For that reason, tools and services compatible with this language are in demand these days. Flask is a lightweight and easy-to-use web framework for Python. It stands out as a lightweight and user-friendly option. Its simplicity and flexibility have made it a popular choice for developers, particularly for creating smaller-scale applications. It is based on the Werkzeug toolkit and provides a simple but powerful API for building web applications. 
Unlike its full-stack counterparts, Flask provides a core set of features, focusing on URL routing, template rendering, and request handling. This minimalist approach makes Flask lightweight and easy to learn, allowing developers to build web applications quickly and without the burden of unnecessary complexity.

Using Flask, REST API, and IAM with InterSystems IRIS

Part 2 – Flask App

Flask is a web development microframework written in Python. It is known for being simple, flexible, and enabling rapid application development.

Installing Flask is very simple. Once you have python installed correctly on your operating system, we need to install the flask library with the pip command. For REST API consumption, it is advisable to use the requests library. The following link provides a guide to installing flask: https://flask.palletsprojects.com/en/stable/installation/

Hey Community!

We're happy to share a new video from our InterSystems Developers YouTube:

⏯  Python Interoperability Productions @ Ready 2025

Hi Community,
In this article, I will introduce my application iris-AgenticAI .

The rise of agentic AI marks a transformative leap in how artificial intelligence interacts with the world—moving beyond static responses to dynamic, goal-driven problem-solving. Powered by OpenAI’s Agentic SDK , The OpenAI Agents SDK enables you to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions. It's a production-ready upgrade of our previous experimentation for agents, Swarm.
This application showcases the next generation of autonomous AI systems capable of reasoning, collaborating, and executing complex tasks with human-like adaptability.

Application Features

• Agent Loop 🔄 A built-in loop that autonomously manages tool execution, sends results back to the LLM, and iterates until task completion.
• Python-First 🐍 Leverage native Python syntax (decorators, generators, etc.) to orchestrate and chain agents without external DSLs.
• Handoffs 🤝 Seamlessly coordinate multi-agent workflows by delegating tasks between specialized agents.
• Function Tools ⚒️ Decorate any Python function with @tool to instantly integrate it into the agent’s toolkit.
• Vector Search (RAG) 🧠 Native integration of vector store (IRIS) for RAG retrieval.
• Tracing 🔍 Built-in tracing to visualize, debug, and monitor agent workflows in real time (think LangSmith alternatives).
• MCP Servers 🌐 Support for Model Context Protocol (MCP) via stdio and HTTP, enabling cross-process agent communication.
• Chainlit UI 🖥️ Integrated Chainlit framework for building interactive chat interfaces with minimal code.
• Stateful Memory 🧠 Preserve chat history, context, and agent state across sessions for continuity and long-running tasks.

Hi Community,

This article is a continuation of my article about  Getting to know Python Flask Web Framework   

In this article, we will cover the basics of topics listed below:

1. Routing in Flask Framework
2. Folder structure for a Flask app (Static and Template)
3. Getting and displaying data in the Flask application from IRIS.

So, let's begin.

Description

With InterSystems IRIS FHIR Server you can build a Strategy to customize the behavior of the server (see documentation for more details).

This repository contains a Python Strategy that can be used as a starting point to build your own Strategy in python.

This demo strategy provides the following features:

• Update the capability statement to remove the Account resource
• Simulate a consent management system to allow or not access to the Observation resource
  • If the User has sufficient rights, the Observation resource is returned
  • Otherwise, the Observation resource is not returned

Hello,

First of all thanks for your time, thoughts, teaching and help:

We wonder how could we get the metrics from http://[Ip]:[Port]/api/monitor/metrics and use them in Kibana to chart statistics.

We would like to ask you how do you use the api monitor metrics, and as a second question, how would you suggest to utilize them in a third party software as Kibana to chart them.
 

We have thought to do the following:

1. Retrieve Metrics from Intersystems HealthShare API: Use a tool or script to make requests to the Intersystems HealthShare REST API and obtain the list of metrics.

Principle: After dividing the article uploaded by the user into sentences using Python, the embedded value is obtained and stored in the Iris database. Then, the similarity between sentences is compared through Iris vector search, and finally displayed on the front-end page.

The installation steps can be viewed in the readme file. It should be noted that the BERT model used in the example has some memory requirements. If there is a long-term stuck situation during the testing process, other models such as MiniLM (which is used in the online demo) can be considered.

Learning LLM Magic

The world of Generative AI has been pretty inescapable for a while, commercial models running on paid Cloud instances are everywhere.  With your data stored securely on-prem in IRIS, it might seem daunting to start getting the benefit of experimentation with Large Language Models without having to navigate a minefield of Governance and rapidly evolving API documentation.   If only there was a way to bring an LLM to IRIS, preferably in a very small code footprint.

Hi, Community!

🐍Great news—you can now use Python to customize your productions and add custom code!

Using Embedded Python in the BPL and DTL Editors

TL;DR: This article demonstrates how to run GraphRAG-style hybrid retrieval—combining vector similarity, graph traversal, and full-text search—entirely within InterSystems IRIS using the iris-vector-graph package. We use a fraud detection scenario to show how graph patterns reveal what vector search alone would miss.

Why Fraud Detection Needs Graphs

Every year, businesses and consumers lose billions to fraud. In 2024 alone, consumers reported $12.5 billion lost—a 25% increase year over year. What makes modern fraud so difficult to detect is that fraudsters rarely work alone.

Hi Community!

Please welcome a new video on InterSystems YouTube Channel:

InterSystems and Python QuickStart

Client: Northwell Health

Role: Senior Developer

Location: Remote

Duration: 6+ Months

Description:

Team Overview:

The FHIR Platform team is tasked with providing the core infrastructure in providing access to Northwell HIE patient data complying with HL7 FHIR and USCDI standards.

Position Summary:

The primary purpose of this role is to provide technical design, coding, testing and documentation for multiple components in the FHIR solution.

Responsibilities:

• Develop end-to-end solutions, participate in code reviews, unit test and deploy.

Enhanced Password Management: Edit Passwords Seamlessly

In the ever-evolving landscape of digital security, robust password management tools have become indispensable. Our password management application, designed to simplify and secure your online life, now comes with an enhanced feature – the ability to edit passwords with ease.

Why is this feature a game-changer?

1. Flexibility: Life is dynamic, and so are our online accounts. With the new edit password feature, you have the flexibility to modify your saved passwords whenever you need to.

Context

The Web Server Gateway Interface (WSGI) is a simple calling convention for web servers to forward requests to web applications or frameworks written in the Python programming language. WSGI is a Python standard described in detail in PEP 3333.

🤔 Ok, great definition and what the point with iris ?

IRIS 2024.2+ has a new feature that allows you to run WSGI applications directly on IRIS. This feature is a great way to integrate IRIS with other Python frameworks and libraries.

I am just writing something to share what I encountered last night, which is the IRIS 2024.3 does not comes with Python by default any more!!!

Which means that I need to install it by myself!!😅 The pros is, I can select my python version😁🤭💃 The trouble is.... at the first place.... I don't know what I should do😥. By going through the community (I am much more prefer than the official document, sorry InterSystems document team😓 ), I found the following piece 

https://docs.intersystems.com/iris20242/csp/docbook/DocBook.UI.

For my intern project, I am building a Flask REST API backend. My goal is to host it on InterSystems IRIS using the WSGI interface. This is a relatively new approach and is currently only being used in a handful of projects such as AskMe. To help others get started, I decided to write this article to simplify the process.

Creating a Basic Flask App

First, let’s create a minimal Flask application. Here is the code:

from flask import Flask
from flask_cors import CORS

app = Flask(__name__)
CORS(app)

@app.route('/test')
def test():
    return "Test"
if __name__ == "__main__":
    app.

Hi Community,

This week we have two new videos from Global Summit 2019. Please welcome:

1.   An ML Toolkit for InterSystems IRIS: Co-Innovation in Healthcare

Hi Developers,

Please welcome the new video, specially recorded by @José Pereira for the InterSystems IRIS AI Programming Contest: 

⏯ Monitoring your ML model with IntegratedML

Interested in sharing your thoughts? Sign up to participate here.

The Rut

Up until early this year, I haven't been not doing much coding at all -- I had gotten sick of it.

After many years as a hands-on software engineer and data scientist, I got burned out around 2015. I switched to business development roles focused on "external innovation," then joined InterSystems in 2019 as a product manager. I missed the creative aspects of coding, but not the tedium. The endless cycle of boilerplate, debugging, and context-switching had left me creatively depleted.

Named parameters can be achieved with SQLAlchemy :  

from sqlalchemy import create_engine, text,types,engine

_engine = create_engine('iris+emb:///')

with _engine.connect() as conn:
    rs = conn.execute(text("select :some_private_name"), {"some_private_name": 1})
    print(rs.all())

or with native api

from sqlalchemy import create_engine, text,types,engine

# set URL for SQLAlchemy
url = engine.url.URL.create('iris', username='SuperUser', password='SYS', host='localhost', port=33782, database='FHIRSERVER')

_engine = create_engine(url)

with _engine.connect() as conn:
    rs = conn.execute(text("select :some_private_name"), {"some_private_name": 1})
    print(rs.all())

Hi,

I am using Windows 10 and I cannot find an installation source for the irisnative python library.  I tried to install the wheel file provided by intersystems on github (https://github.com/intersystems/quickstarts-python/).

c:\wc\quickstarts-python\Solutions\nativeAPI_wheel>pip install irisnative-1.0.0-cp34.cp35.cp36.cp37.cp38.cp39-none-win_amd64.whl
ERROR: irisnative-1.0.0-cp34.cp35.cp36.cp37.cp38.cp39-none-win_amd64.whl is not a supported wheel on this platform.
 

Is irisnative supported on Windows 10 ?  The purpose is to execute ObjectScript command from external python code.